Biomedical subjects

Jian Gao

Publications and source records attributed to Jian Gao.

4 recordsLinked to original sources

PubMed2026

Genomic analysis of regulatory mechanisms governing EPS66A biosynthesis in Streptomyces changanensis HL-66.

Streptomyces changanensis HL-66 produces the α-(1,4)/(1,6)-glucan exopolysaccharide EPS66A, a potent plant immune elicitor with promising applications in plant protection. However, its low native fermentation yield limits large-scale application. To investigate the biosynthetic potential and regulatory mechanisms underlying EPS66A production, the whole genome of HL-66 was sequenced and analyzed. The HL-66 genome is 6.82 Mb in size, with a GC content of 74%, and encodes 6081 predicted functional genes. Among these, 1390 genes were annotated to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, 4187 were assigned to Gene Ontology (GO) terms, and 143 were classified into Clusters of Orthologous Groups (COG) categories. antiSMASH analysis identified 22 secondary metabolite biosynthetic gene clusters, including multiple polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) clusters. Functional analyses revealed that the glycosyltransferase gene (GTy) and the global regulatory gene (bldD) are involved in EPS66A biosynthesis. bldD is involved in morphological development and EPS66A production, whereas GTy specifically regulates EPS66A production without affecting growth or development. In both in vivo and potted-plant experiments, EPS66A (200 μg/mL) significantly reduced the severity of tobacco mosaic virus, apple anthracnose leaf spot, walnut bacterial leaf spot, and jujube anthracnose, achieving control efficacies of 90.21%, 87.95%, 77.41%, and 68.55%, respectively, and outperforming a commercial chitosan oligosaccharide control. These findings provide new insights into the genetic architecture and regulatory mechanisms of EPS66A biosynthesis and support its development as a polysaccharide-based green pesticide.

Streptomyces↗

PubMed2026

Genomic and food-safety evaluation of Staphylococcus chromogenes in Chinese dairy milk.

Non-aureus staphylococci and mammaliicocci (NASM) cause mastitis and may contaminate milk and dairy products. Milk samples (n = 1916) from cows with subclinical or clinical mastitis (SCM and CM, respectively) were collected from 28 large-scale (> 500 lactating cows) Chinese dairy farms. Overall, 999 NASM isolates representing 19 species were identified by MALDI-TOF MS and cpn60 sequencing, with Staphylococcuschromogenes, Mammaliicoccus sciuri and Staphylococcus haemolyticus being most prevalent. Antimicrobial resistance (AMR) was determined with disc diffusion; non-susceptible to penicillin was most common (SCM, 30% and CM, 29%) whereas cefoxitin non-susceptible NASM accounted for 8-10% of isolates; among these, 12.5% carried mecA but none carried mecC. Galleria mellonella was used to assess virulence of 78 strains of S. chromogenes, a dominant species; subsequently, 32 strains, representing higher- and lower-virulence in the Galleria model, were selected for whole-genome sequencing and comparative genomics. S. chromogenes isolates from CM had higher virulence (p < 0.05) than those from SCM. The 32 genomes comprised 20 sequence types, indicating high genetic diversity. No robust genomic marker of Galleria virulence phenotype was identified in this selected WGS subset. Acquired resistance genes (n = 5) were detected, including a first report of fusC in S. chromogenes; the fusC-positive isolate had an elevated fusidic acid MIC (8 mg/L). Although S. chromogenes persisted in milk at 4 °C, pasteurization (64 °C for 30 min) reduced viable counts to below detection. This study provided new insights into the prevalence, AMR, genomic diversity, and dairy-chain relevance of milk-derived NASM, particularly S. chromogenes. However, the genomic findings were based on an intentionally selected WGS subset and should be interpreted as hypothesis-generating rather than population-representative.

Animals↗

PubMed2026

Structure-based drug design of small-molecule c-Myc G-quadruplex binders.

The c-Myc oncogene is crucial in tumorigenesis. Although it is a promising therapeutic target, its protein lacks a conventional drug-binding pocket, making it traditionally "undruggable". Recent studies show that the c-Myc promoter can form a G-quadruplex (G4) structure, which suppresses transcription and offers a new strategy for indirect inhibition. In this study, structure-based virtual screening was performed using the c-Myc G4 crystal structure to screen the ChemDiv compound library, aiming to identify small molecules that bind to the G4 structure. Candidate compounds were evaluated in preliminary in vitro assays for biological activity. The results showed that Y502-3888 binds to the c-Myc G4 and downregulates c-Myc expression at both mRNA and protein levels. Collectively, these findings support the potential of Y502-3888 as a c-Myc G4 binder for the treatment of multiple myeloma (MM), providing a foundation for future development of anticancer agents targeting the c-Myc G4.

G-Quadruplexes↗

PubMed2026

Integrative Multi-Omics Mendelian Randomization Analysis Identifies NIT2 as a Potential Metabolic Risk Gene in Hepatocellular Carcinoma.

BACKGROUND: Metabolic pathways are crucial in hepatocellular carcinoma (HCC) pathogenesis, but causal metabolic genes remain unclear. This study used Summary data-based Mendelian Randomization (SMR) and colocalization to identify metabolism-related genetic loci influencing HCC risk. METHODS: Differentially expressed genes in hepatic malignancy phenotype versus normal tissues from TCGA and GTEx were analyzed. Metabolism-related candidates were examined via SMR and colocalization using multi-omics data: methylation (mQTL), expression (eQTL), and protein (pQTL) quantitative trait loci. RESULTS: Multi-omics integration identified NIT2 as a key metabolic regulator for HCC. The cg13016775 locus of NIT2 was associated with elevated HCC risk at gene (OR = 1.618, 95% CI: 1.199-2.182) and protein (OR = 4.432, 95% CI: 1.783-11.018) levels. Colocalization supported a shared causal variant (PPH4 > 0.6), linking NIT2 to hepatocarcinogenesis via metabolic regulation. CONCLUSIONS: This study provides multi-omics evidence for NIT2 as a potential causal gene in HCC, enhancing understanding of metabolic contributions to HCC pathogenesis and highlighting integrative genomics for uncovering causal relationships.

Carcinoma, Hepatocellular↗